Recognition and segmentation of complex texture images based on superpixel algorithm and deep learning

Recognizing and segmenting complex texture images such as materials is of great significance to industrial design and production. Due to the lack of sufficient training samples and fuzzy boundaries in material images, it is difficult to segment material images by using deep learning methods. In material images, the pixels of each phase have a high degree of similarity, so if partial pixels' features in each phase are learned, the whole phase can be recognized. In this paper, we propose a method based on deep learning for recognizing and segmenting material images with complex textures. Firstly, the simple linear iterative cluster(SLIC) algorithm is used to obtain different numbers of superpixels which are a group of pixels with similar texture features. Then we extract the largest inscribed rectangular block in each superpixel. Next, put these rectangular blocks into the classical convolutional neural network(CNN)-DenseNet to recognize them. To retain the key texture features and reduce redundant information, we increase the receptive field in the key layers of DenseNet. In addition, due to the uneven distribution of phases in the material images, we improve focal loss to fit the material image. We make extensive comparative and ablation experiments to confirm the effectiveness of our method.